44 phd-position-in-computer-vision PhD positions at Cranfield University in United-States
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. The integration of AI into hardware not only enhances performance but also reduces energy consumption, addressing the growing demand for sustainable and efficient computing solutions. This PhD project delves
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We are looking for a highly motivated candidate to pursue a PhD programme titled "CFD-informed finite element analysis for thermal control in wire-arc directed energy deposition." This research
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This research opportunity invites self-funded PhD candidates to develop advanced deblurring techniques for retinal images using deep learning and variational methods. Retinal images often suffer
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constructed to support the researcher as the PhD progresses with specific courses aimed at the different phases of a PhD. For example, the programme includes aspects such as research methods, technical report
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This PhD project will focus on developing, evaluating, and demonstrating a framework of novel hybrid prognostics solution for selected system use case (e.g. clogging filter, linear actuator, lithium
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Embark on a ground-breaking PhD project harnessing the power of Myopic Mean Field Games (MFG) and Multi-Agent Reinforced Learning (MARL) to delve into the dynamic world of evolving cyber-physical
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. At a glance Application deadline26 Nov 2025 Award type(s)PhD Start date26 Jan 2026 Duration of award3 years EligibilityUK, EU, Rest of world Reference numberCRAN-0003 Entry requirements Applicants
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complimentary computational studies to predict the intake aerodynamic characteristics and aid in the experiment design. This position is part of the CDT in Net Zero Aviation, which offers a modular, cohort-based
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, multidisciplinary PhD research projects across areas such as: Zero Emission Technologies. Ultra Efficient Aircraft, Propulsion, Aerodynamics, Structures and Systems. Aerospace Materials, Manufacturing, and Life Cycle
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, these systems serve as complex functional approximators trained over an input-output data set. ‘Second Wave AI’ is the term used to describe the current glut of 'machine learning' style intelligence, where